MLS-C01 Modeling Practice Question
Network Topology
Refer to the exhibit. A data scientist is using Amazon SageMaker Ground Truth to label a dataset. The output manifest file references S3 objects with metadata. The scientist notices that a training job using the labeled data yields poor accuracy. What is the most likely issue?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The training data is in an incorrect format for the algorithm.
The poor accuracy is most likely due to the training data being in an incorrect format for the algorithm. Amazon SageMaker Ground Truth outputs a manifest file with metadata, but the source S3 objects may be in a format (e.g., raw images, text files) that is not directly compatible with the chosen built-in algorithm or custom model. For example, if the algorithm expects RecordIO-encoded data or a specific CSV structure, but the manifest points to raw JPEG images, the training job will still run (no failure) but produce poor results. Other options: missing labels or IAM issues would typically cause job failures, not just poor accuracy; data distribution shift is possible but less directly indicated by the exhibit.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The labeled dataset has missing labels for some records.
Why it's wrong here
Missing labels would cause training errors, not just poor accuracy.
- ✓
The training data is in an incorrect format for the algorithm.
Why this is correct
If the data format does not match the algorithm's expectations, training may complete but produce poor results.
- ✗
The IAM role used for training does not have permissions to read the manifest file.
Why it's wrong here
Permission issues would cause access denied errors, not poor accuracy.
- ✗
The data distribution differs significantly between the training set and the real-world inference data.
Why it's wrong here
Data distribution shift is a common cause of poor accuracy, but the question is about the labeled data itself, not inference.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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